DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 7/10/2023 and 9/15/2023 comply with the provisions of 37 CFR 1.97 and are being considered.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
The “provisioning unit,” the “pre-processing unit,” and the “detection unit” in Claim 5. Enough corresponding structure is disclosed in the instant Specification for one having ordinary skill in the art to understand that these “units” correspond to general-use computer processing hardware elements and machine executable code.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recite sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
See MPEP 2106 for details. The following is the two-prong analysis for subject matter eligibility:
Claim 1:
The claim limitations in the abstract idea have been highlighted in bold below; the remaining limitations are “additional elements.”
“A method for detecting anomalies on a surface of an object, the method comprising: creating a depth profile of the surface of the object; pre-processing the depth profile by approximating a shape along a spatial dimension and subsequently subtracting the approximated shape from the depth profile in order to obtain a simplified profile; and detecting the anomalies on the surface of the object by applying a machine learning algorithm to the simplified profile, the machine learning algorithm trained in order to detect anomalies in depth profiles.”
Step 1:
Claim 1 describes a method and falls under the four statutory categories.
Step 2A - Prong One:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The claimed invention is directed to an abstract idea without significantly more.
The bold claim elements in Claim 1 above, namely “pre-processing,” “approximating,” “subtracting,” and “detecting” are data observation, processing, and analysis techniques that are considered to be both mental processes and mathematical calculations and are capable of being performed mentally or with the aid of pen and paper.
Step 2A - Prong Two:
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exceptions into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
Merely implementing Claim 1’s judicial exceptions upon a generic “object,” “object depth profile,” or “simplified profile” does not add a meaningful limitation to the above-cited claim elements sufficient to bring the exceptions into practical application. Specifically, implementing the aforementioned abstract ideas on a generic object/profile amounts to no more than an application of the judicial exception (See MPEP 2106.05(f)). Similarly, “applying a machine learning algorithm” to detect anomalies is a generic application and as well, as the machine learning algorithm is merely performing the detection in place of a human performing a mental process.
Noting MPEP 2106.04(d)(I): “It is notable that mere physicality or tangibility of an additional element or elements is not a relevant consideration in Step 2A Prong Two. As the Supreme Court explained in Alice Corp., mere physical or tangible implementation of an exception does not guarantee eligibility. Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208, 224, 110 USPQ2d 1976, 1983-84 (2014) ("The fact that a computer ‘necessarily exist[s] in the physical, rather than purely conceptual, realm,’ is beside the point")”.
Training the machine learning algorithm to perform the function of detecting anomalies is insignificant extra-solution activity. It is inherent that a machine learning algorithm used for the purpose of detecting anomalies would have to have been trained to do so, so the training element does not add a further limitation to integrate this claim into practical application (see MPEP 2106.05(g)).
Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception of Claim 1 into a practical application and the claim is directed to the judicial exception.
Step 2B:
The additional elements of Claim 1 as described above with respect to Step 2A Prong 2 are not sufficient to amount to significantly more than the judicial exception. Namely the application of the machine earning algorithm on the object profile, the simplified profile, and the object itself, do not provide sufficient structural detail to render them any more than attempts to apply the abstract idea in a technological environment on a generic “object.”
The training of the machine learning algorithm is insignificant extra-solution activity and, when re-evaluated under Step 2B is further found to be well-understood, routine, and conventional in the field of defect detection with machine learning algorithms. See paragraphs [0003-0005] of the instant applications specification for an explanation of the use of machine learning algorithms in the field of “quality assurance during a manufacturing process,” wherein it is stated that “such methods for inspecting the surface of manufactured components are often based on machine learning algorithms…wherein the generated depth profile can then be evaluated, for example based on a correspondingly trained machine learning algorithm.” These quotations and the passages from which they are taken clearly establish what is well-understood, conventional, and routine in this field.
Therefore, the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that Claim 1 amounts to significantly more than the abstract idea.
Regarding dependent Claims 2 and 4:
Claims 2 and 4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Continuing with the 2-Prong Analysis for the sake of compact prosecution:
Step 1:
The claims fall within at least one of the four categories of patent eligible subject matter because they are methods.
Step 2A – Prong 1:
Principal component analysis and polynomial expansion are abstract ideas, both combinations of mental processes and mathematical calculations with no specialized structure or technology recited to perform the principal component analysis.
Step 2A – Prong 2:
Claims 2 and 4 do not contain any additional elements and thus cannot be rendered into practical application.
Step 2B:
Again, Claims 2 and 4 do not contain any additional elements and thus cannot amount to more than the judicial exceptions.
Regarding the dependent Claim 3:
“The method according to claim 2, wherein: the pre-processing the depth profile further includes additionally simplifying the simplified profile by subtracting a plurality of principal components of the principal component analysis in order to obtain an additionally simplified profile, and the machine learning algorithm is applied to the additionally simplified profile in order to detect the anomalies.”
Step 1:
Claim 3 is a method claim and thus falls under one of the four statutory categories.
Step 2A - Prong 1:
The claimed invention is directed to an abstract idea without significantly more. Considering its dependence on Claim 1, Claim 3 only recites one additional limitation: "subtracting a plurality of principal components of the principal component analysis." The principal component analysis is a data analysis and dimensionality reduction technique and thus both a mental process, capable of being performed with pen and paper, and a mathematical calculation.
Step 2A - Prong 2:
This judicial exception is not integrated into a practical application because neither specialized structure nor function is recited to subtract the principal components or implement the machine learning algorithm. The implementation of the abovementioned claim element on the “depth profile” is a generic application of the abstract idea, similar to the depth profile implementation in Claim 1, and thus subject to the same analysis and conclusion under Step 2A Prong 2.
Step 2B:
Again, the implementation of the principal component subtraction on the depth profile is subject to the same analysis as Claim 1, and thus the claim does not include additional elements (the “depth profile”) that are sufficient to amount to significantly more than the judicial exception.
Regarding the dependent Claim 5:
“The method according to claim 1, wherein: a controller is configured to perform the method, and the controller is configured to implement: a provisioning unit configured so as to provide the depth profile of the surface of the object, a pre-processing unit configured to pre-process the depth profile, and a detection unit configured to detect the anomalies on the surface of the object by applying the machine learning algorithm to the simplified profile.”
Step 1:
Claim 5 is a method and apparatus and thus one of the four statutory categories.
Step 2A - Prong 1:
The claimed invention is directed to an abstract idea without significantly more. The bold elements in Claim 5 above, namely “provide,” “pre-process,” and “detect” are, similar to Claim 1-: data observation, processing, and analysis techniques that are considered to be both mental processes and mathematical calculations and subject to the same Step 2A Prong 1 analysis and conclusions as in Step 1.
Step 2A - Prong 2:
In addition to the abstract ideas recited in Claim 5, the claimed method recites elements, including the controller, provisioning unit, pre-processing unit, and detection unit, which are described with a high level of generality in the claims and specification (see Fig. [4] and paragraphs [0087-0090]), These are recitations of non-specialized technology and are thus found to be equivalent to adding the words “apply it,” and mere instructions to apply a judicial exception on a general purpose computer do not integrate the abstract idea into a practical application (See MPEP 2106.05(f)). Thus, the “additional elements” of Claim 5 are subject to the same analyses and is found to be ineligible for the same reasons as those of Claim 1.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because each structural element, namely the controller, the provisioning unit, the pre-processing unit, and the detection unit, is a recitation of generic technology, defined only by its implementation of the judicial exceptions in the claims. When combined with the judicial exceptions, none of these additional elements performs a function that amounts to more than the judicial exception.
With regards to the dependent claims, Claims 6, 7, and 8 merely further expand upon the abstract ideas identified in Claim 5. The judicial exceptions (“to apply a principal component analysis" and "subtracting a plurality of principal components”, and “apply a polynomial expansion”) and additional elements (“pre-processing unit”, “detection units”, “simplified profile”) in these three claims have been addressed in the 2-prong analysis for Claims 1, 3, and 5 and do not set forth further additional elements that integrate the recited abstract idea into a practical application or amount to significantly more. Therefore, these claims are found ineligible for the reasons described for parent claims 1 and 5.
Regarding Claim 9:
“A method for discarding objects of a plurality of objects, comprising: for each object of the plurality of objects, respectively detecting anomalies on a surface of the object in question by: creating a depth profile of the surface of the object, pre-processing the depth profile by approximating a shape along a spatial dimension and subsequently subtracting the approximated shape from the depth profile in order to obtain a simplified profile, and detecting the anomalies on the surface of the object by applying a machine learning algorithm to the simplified profile, the machine learning algorithm trained in order to detect anomalies in depth profiles; for each object of the plurality of objects, respectively determining whether the object in question is to be discarded based on the anomalies detected on the surface of the object; and for each object of the plurality of objects, respectively discarding the object in question when it has been determined that the object in question is to be discarded.”
Step 1:
Claim 9 is a method claim and thus one of the four statutory categories.
Step 2A - Prong 1:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The limitations recited in Claim 9 are similar in scope to those of Claim 1, with one additional limitation directed to a judicial exception: "determining whether the object in question is to be discarded…," a form of classification and thus a mental process subject to a similar analysis to the mental processes identified in Claim 1.
Step 2A - Prong 2:
This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exceptions into a practical application of the exception. This evaluation is performed by (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (b) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application.
In addition to the additional elements identified in Claim 1, Claim 9 further recites the additional element: “discarding the object in question…” The judicial exception of Claim 9 is not integrated into a practical application because the additional elements, including the discarding step, are not recited in enough detail to bring the abstract ideas into practical application. The “discarding” step is an insignificant extra-solution activity to the “determining” step, an extension of the mental process of classification. Thus, under Step 2A, prong 2 of the analysis, even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
Step 2B:
The claim is subject to the 2-Prong Analysis performed on Claim 1 and the conclusions drawn therein. The use of the machine learning algorithm to discard defective objects is a well-understood, routine, and conventional function in the art of manufacturing quality monitoring/assurance using machine learning techniques. This is stated in paragraph [0003-0005] of the specification: “in the context of quality assurance during a manufacturing process, objects or components are typically subjected to an inspection…it can be decided, for example, whether the object in question is to be readily further processed and used, or else scrapped or disposed of…” Therefore, similarly the above identified additional element when analyzed under Step 2B also fails to necessitate a conclusion that Claim 9 amounts to significantly more than the abstract idea.
Regarding Claim 10:
“A system for detecting anomalies on a surface of an object, comprising: a measurement system configured to generate a depth profile of the object; and a controller operably connected to the measurement system and configured to detect the anomalies on the surface of the object, the controller configured to implement: a provisioning unit configured to provide the depth profile of the surface of the object, a pre-processing unit configured to pre-process the depth profile, and a detection unit configured to detect the anomalies on the surface of the object by applying a machine learning algorithm to the simplified profile, wherein the controller is configured to process the depth profile generated by the measurement system in order to detect the anomalies on the surface of the object.”
Step 1:
Claim 10 is an apparatus claim and thus directed to one of the four statutory categories.
Step 2A - Prong 1:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
All elements of Claim 10 that are directed to judicial exceptions have been addressed in the 2-Prong analysis for Claim 1.
Step 2A - Prong 2:
This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
In addition to those listed under the Claim 1 analysis, Claim 10 further recites one “additional” element: "a measurement system configured to generate a depth profile…” The judicial exceptions are not integrated into a practical application because the additional elements, generically link the judicial exceptions without adding meaningful limitations to the claim elements (See MPEP 2106.05) The claimed measurement system could be any system, method, or apparatus that produces a depth profile, the substitution of which would have no impact on the detection of anomalies. All other additional elements have been addressed in the analysis for Claim 1.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because each “additional” element described above with respect to 2A – Prong 2, namely the “measurement system,” is a recitation of a generic technology, defined only by its implementation of the judicial exceptions in the claims. While the specification does propose a “laser measurement system (See spec. paragraph [0007]), under Broadest Reasonable Interpretation, any method of measuring can be used that would result in the creation of a depth profile. This “additional” element is thus too generic to render the judicial exceptions into practical use, and when combined with the judicial exceptions, does not amount to significantly more than the exceptions.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 2, 5, 6, and 10 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by Noone et. al (US 2020/0166909 A1). The bolded portions of the Noone quotations below are directly analogous to claim elements in the instant application.
Regarding Claim 1:
Noone et. al teaches: a method for detecting anomalies on a surface of an object, the method comprising: creating a depth profile (see fig. [6B]) of the surface of the object (para. [0007, 0009, 0017], fig. [6B], see below:
“In some embodiments, the one or more manufacturing process characterization sensors comprise at least one laser interferometer, machine vision system, or sensor that detects electromagnetic radiation that is reflected, scattered, absorbed, transmitted, or emitted by the object. In some embodiments, the one or more process control parameters are adjusted at a rate of at least 100 Hz.” (para. [0007])
“In some embodiments, the manufactured object defects are detected and classified using….” (para. [0009])
“FIGS. 6A-B illustrate one non-limiting example of in-process feature monitoring using interferometry. FIG. 6A: schematic illustration of laser beams used to probe the geometry of the wire feed and melt pool overlaid with a photo of a laser-metal wire deposition process. FIG. 6B: cross-sectional profiles (i.e., height profiles across the width of the deposition) of the wire feed (solid line; peak) and previously deposited layer (solid line; shoulders) and resulting melt pool (dashed line). The x-axis (width) dimension is plotted in arbitrary units. The y-axis (height) dimension is plotted in units of millimeters relative to a fixed reference point below the deposition layer.” (para. [0017]));
Noone et. al teaches: pre-processing the depth profile by approximating a shape along a spatial dimension (see para. [0224]): “For example, a reference data set may comprise sensor data recorded by one or more sensors for an ideal, defect-free example of the object to be fabricated.” The reference data set for an ideal object being a “shape” – see fig. 6B) and subsequently subtracting the approximated shape from the depth profile in order to obtain a simplified profile (para. [0017, 0210, 0222-0224], fig. [6B], see below:
“Automated image processing of the captured images may then be used to monitor any of a variety of object properties, e.g., dimensions (overall dimensions, or dimensions of specific features), feature angles, feature areas, surface finish (e.g., degree of light reflectivity, number of pits and/or scratches per unit area), and the like.” (para. [0222]).
“In some embodiments, the automated defect classification methods may further comprise removing noise from the object property data provided by the one or more sensors prior to providing it to the machine learning algorithm. Examples of data processing algorithms suitable for use in removing noise from the object property data provided by the one or more sensors include, but are not limited to, signal averaging algorithms, smoothing filter algorithms, Kalman filter algorithms, nonlinear filter algorithms, total variation minimization algorithms, or any combination thereof.” (para. [0223])
“FIG. 6B provides examples of cross-sectional profiles (i.e., height profiles across the width of the deposition) of the wire feed, previously deposited layer, and melt pool as measured using laser interferometry at the position of the wire feed (solid line; the peak indicates the wire, while the shoulders indicate the height of the previously deposited layer) and the melt pool (dashed line). The x-axis (width) dimension is plotted in arbitrary units. The y-axis (height) dimension is plotted in units of millimeters relative to a fixed reference point below the deposition layer.” (para. [0210])
“Subtraction of reference data sets: In some embodiments of the disclosed automated defect classification methods, subtraction of a reference data set from the sensor data may be used to increase contrast between normal and defective features of the object, thereby facilitating defect detection and classification.” (para. [0224]));
Noone et. al teaches: detecting the anomalies on the surface of the object by applying a machine learning algorithm to the simplified profile, the machine learning algorithm trained in order to detect anomalies in depth profiles, (para. [0225-0229, 0239], see below:
“Machine learning algorithms for defect detection and classification: Any of a variety of machine learning algorithms may be used in implementing the disclosed automated object defect detection and classification methods. The machine learning algorithm employed may comprise a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a reinforcement learning algorithm, a deep learning algorithm, or any combination thereof. In preferred embodiments, the machine learning algorithm employed for defect identification and classification may comprise a support vector machine (SVM), an artificial neural network (ANN), or a decision tree-based expert learning system, some of which will be described in more detail below. In some preferred embodiments, object defects may be detected as differences between an object property data set and a reference data set that are larger than a specified threshold, and may be classified using a one-class support vector machine (SVM) or autoencoder algorithm. In some preferred embodiments, object defects may be detected and classified using an unsupervised one-class support vector machine (SVM), autoencoder, clustering, or nearest neighbor (e.g., kNN) machine learning algorithm and a training data set that comprises object property data for both defective and defect-free objects.” (para. [0225])
“As noted above, the machine learning algorithm(s) employed in the disclosed automated defect classification and additive manufacturing process control methods may comprise a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm, a reinforcement learning algorithm, a deep learning algorithm, or any combination thereof.” (para. [0239]) ).
Regarding Claim 2:
Noone et. al paragraphs [0247, 0260] teach: the method according to claim 1, wherein the pre-processing the depth profile includes applying a principal component analysis.
“Autoencoders are often used for the purpose of dimensionality reduction, i.e., the process of reducing the number of random variables under consideration by deducing a set of principal component variables. Dimensionality reduction may be performed, for example, for the purpose of feature selection (i.e., a subset of the original variables) or feature extraction (i.e., transformation of data in a high-dimensional space to a space of fewer dimensions).” (para. [0247])
“For distributed systems, the sharing of data between one or more manufacturing apparatus, one or more process monitoring sensors, machine vision systems, and/or in-process inspection tools may be facilitated through the use of a data compression algorithm, a data feature extraction algorithm, or a data dimensionality reduction algorithm.” (para. [0260])
Regarding Claim 5:
Noone et. al teaches: the method according to claim 1, wherein: a controller is configured to perform the method, see para. [0265] below.
“The one or more processors, e.g., a CPU, execute a sequence of machine-readable instructions, which are embodied in a program (or software). The instructions are stored in a memory location. The instructions are directed to the CPU, which subsequently program or otherwise configure the CPU to implement the methods of the present disclosure. Examples of operations performed by the CPU include fetch, decode, execute, and write back. The CPU may be part of a circuit, such as an integrated circuit.” (para. [0265])
Noone et. al teaches: the controller is configured to implement: a provisioning unit configured so as to provide the depth profile of the surface of the object, see para. [0230] below, in light of para. [0265].
“Any of a variety of sensors, measurement tools, or inspection tools may be used for monitoring various manufacturing process parameters in real- time, including those listed above. In some embodiments, for example, laser interferometers are used to monitor the dimensions…or other part dimensions as the part is being fabricated. In some embodiments, the one or more sensors (e.g., image sensors, cameras, or machine vision systems) provide… In some embodiments, the one or more sensors may provide process characterization data to the processor programmed to run the machine learning algorithm may be updated at a rate of at least…” (para. [0230])
Noone et. al teaches: a pre-processing unit configured to pre-process (see para. [0267], refer to para. [0223,0224]) the depth profile, see below.
“Some aspects of the methods and systems provided herein, such as the disclosed object defect classification or manufacturing process control algorithms, are implemented by way of machine (e.g., processor) executable code stored in an electronic storage location of the computer system, such as, for example, in the memory or electronic storage unit. The machine executable or machine readable code is provided in the form of software. During use, the code is executed by the one or more processors. In some cases, the code is retrieved from the storage unit and stored in the memory for ready access by the one or more processors.” (para. [0267])
Noone et. al teaches: a detection unit configured to detect (see para. [0262]) the anomalies on the surface of the object by applying the machine learning algorithm to the simplified profile, see below.
“One or more processors may be employed to implement the machine learning algorithms, automated object defect classification methods, and manufacturing process control methods disclosed herein.” (para. [0262])
Regarding Claim 6:
Noone et. al teaches: the method according to claim 5, wherein the pre-processing unit is configured to apply a principal component analysis in order to pre-process the depth profile. See para. [0247, 0260], above, for the use of principal component analysis to pre-process the depth profile and para. [0267], also above, for reference to the pre-processing unit.
Regarding Claim 10:
Noone et. al teaches: a system for detecting anomalies on a surface of an object, comprising: a measurement system configured to generate a depth profile of the object, see para. [0275] below:
"…the expected outcome for an unsupervised machine learning process for classification of object defects. One or more automated inspection tools, e.g., machine vision systems coupled with automated image processing algorithms, are used to monitor and measure feature dimensions, angles, surface finishes, and/or other properties of fabricated parts…. Defects may be identified, e.g., by removing noise from the inspection data and subtracting a reference data set…and classified using an unsupervised machine learning algorithm…" (para. [0275])
Noone et. al further teaches: a controller operably connected to the measurement system and configured to detect the anomalies on the surface of the object, the controller configured to implement a provisioning unit configured to provide the depth profile of the surface of the object. See para. [0230, 0265] above, specifically the bold portions, which identify “sensors” and “measurement tolls” which provide object data to the “processor programmed to run the machine learning algorithm,” as well as a CPU (the controller).
Noone et. al teaches: a pre-processing unit configured to pre-process the depth profile. See the bold portions of para. [0267] above for the method being performed via machine (pre-processing unit) executable code, and refer to para. [0223, 0224] for the pre-processing steps.
Noone et. al further teaches: a detection unit configured to detect the anomalies on the surface of the object by applying a machine learning algorithm to the simplified profile, wherein the controller is configured to process the depth profile generated by the measurement system in order to detect the anomalies on the surface of the object, see para. [0225-0229, 0239, 0262] above for a description of machine learning algorithms, the use of a machine learning algorithm to detect anomalies (defects), and the processor (detection unit) that implements the anomaly-detecting machine learning algorithm, respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Noone et. al in view of Turner et. al. (US 20220155694 A1).
Regarding Claim 3:
Noone et. al discloses the bold portions of Claim 3: “the method according to claim 2, wherein: the pre-processing the depth profile further includes additionally simplifying the simplified profile by subtracting a plurality of principal components of the principal component analysis in order to obtain an additionally simplified profile, and the machine learning algorithm is applied to the additionally simplified profile in order to detect the anomalies.” See para. [0247, 0260] for the use of principal component analysis pre-processing of the depth profile. See para. [0225-0229, 0239] for the application of the machine learning algorithm for anomaly detection.
Noone et. al. does not disclose, in this paragraph, the bold portions of Claim 3: “the method according to claim 2, wherein: the pre-processing the depth profile further includes additionally simplifying the simplified profile by subtracting a plurality of principal components of the principal component analysis in order to obtain an additionally simplified profile, and the machine learning algorithm is applied to the additionally simplified profile in order to detect the anomalies.”
Turner et. al. discloses the bold portions of Claim 3: “the method according to Claim 2, wherein: the pre-processing the depth profile further includes additionally simplifying the simplified profile by subtracting a plurality of principal components (see para. [0112-0113]) of the principal component analysis in order to obtain an additionally simplified profile (see para. [0112-0113]), and the machine learning algorithm is applied to the additionally simplified profile (see para. [0112-0113]) in order to detect the anomalies.” See also para. [0034, 0094-0099, 0104-0105] for context.
“In order that the substrates that are exposed by the lithographic apparatus are exposed correctly and consistently, it is desirable to inspect exposed substrates to measure properties such as overlay errors between subsequent layers, line thicknesses, critical dimensions (CD), etc.” (para. [0034])
“Further embodiments of the invention are disclosed in the list of numbered clauses below: 1. A method of determining whether a substrate or substrate portion is subject to a process effect, the method comprising: obtaining inspection data comprising a plurality of sets of measurement data associated with a structure on the substrate or portion thereof; obtaining fingerprint data describing a spatial variation of a parameter of interest over a substrate or portion thereof; performing an iterative mapping of the inspection data to the fingerprint data; and determining whether the substrate is subject to a process effect based on the degree to which the iterative mapping converges on a solution.” (para. [0094-0099])
“6. A method according to any preceding clause, wherein said fingerprint data has been obtained from previous measurement data relating to at least one previous measurement of the known process effect. 7. A method according to clause 6, wherein said fingerprint data comprises a principal component of said previous measurement data.” (para. [0104-0105])
“14. A method according to clause 13, wherein said correction for a wafer background fingerprint comprises performing a component analysis. 15. A method according to clause 14, wherein said correction for a wafer background fingerprint comprises removing one or more principal components other than the first principal component, when determining an updated fingerprint for each iteration of the iterative mapping.” (para. [0112-0113]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to repeat, as taught by Turner, the subtraction of principal components of Noone in order to further reduce the data dimensionality and improve error detection.
Regarding Claim 7:
Noone et. al discloses the bold portions of Claim 7: “the method according to claim 6, wherein: the pre-processing unit is configured to further simplify the simplified profile by subtracting a plurality of principal components of the principal component analysis in order to obtain an additionally simplified profile, and the detection unit is configured to apply the machine learning algorithm to the additionally simplified profile in order to detect the anomalies.”
Noone et. al. does not disclose, in this paragraph, the bold portions of Claim 7: “the method according to claim 6, wherein: the pre-processing unit is configured to further simplify the simplified profile by subtracting a plurality of principal components of the principal component analysis in order to obtain an additionally simplified profile, and the detection unit is configured to apply the machine learning algorithm to the additionally simplified profile in order to detect the anomalies.”
Turner et. al. discloses the bold portions of Claim 7: “the method according to claim 6, wherein: the pre-processing unit is configured to further simplify the simplified profile by subtracting a plurality of principal components of the principal component analysis in order to obtain an additionally simplified profile (see para. [0112-0113], above), and the detection unit is configured to apply the machine learning algorithm to the additionally simplified profile (see para. [0112-0113], above) in order to detect the anomalies.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to repeat, as taught by Turner, the subtraction of principal components of Noone, using the pre-processing unit taught by Noone, in order to further reduce the data dimensionality and improve error detection.
Claims 4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Noone et. al. in view of Lukas-Simonyi et. al (DE 3801297 A1).
Regarding Claim 4:
Noone et. al fails to disclose: The method according to claim 1, wherein the pre-processing the depth profile includes applying a polynomial approximation.
Paragraphs [0002] and [0003] of Lukas-Simonyi et. al, which is a reference disclosed by the applicants, teach: The method according to claim 1, wherein the pre-processing the depth profile includes applying a polynomial approximation. See the quotation below, specifically the bold portions.
“One example of such a stochastic measurement is the measured temperature of an engine blade during flight operation. In this process, the measured temperature values are plotted on a coordinate system as a function of time and represented as a stochastic measurement curve. Another application is when the surface microstructure of a workpiece needs to be recorded as accurately as possible, including any so-called [missing information] directed away from the surface… To assess roughness, that is, the surface texture of technical surfaces, roughness parameters are generally used, which are determined with stylus instruments.” (para. [0002-0003])
See further, paragraph [0022] and Claims 1-10, 15, and 16. The following is a translation of Claim 1 of Lukas-Simonyi.
“A method for measuring a stochastic quantity with a measuring device, of which a large number of measured values are acquired with a sensor and stored in a memory of the measuring device, wherein the successive measured values form a measurement curve in the form of a stochastic curve, wherein this curve is decomposed by means of a microprocessor and an algorithm into one or more long-wavelength components corresponding to the ripple and one or more short-wavelength, purely stochastic components, characterized in that the measurement curve is represented in the form of at least one trigonometric approximation or interpolation polynomial….”
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply a polynomial approximation taught in Lukas-Simonyi to the profiles of Noone (see fig. [6B] of Noone) in order to better characterize the profiles.
Regarding Claim 8:
Noone et. al fails to disclose: “The method according to claim 5, wherein the pre-processing unit is configured to apply a polynomial approximation in order to pre-process the depth profile.”
Paragraphs [0002, 0003, 0022], and Claims 1-10, 15, and 16 of Lukas-Simonyi et. al, which is a reference disclosed by the applicants, teach: The method according to claim 1, wherein the pre-processing the depth profile includes applying a polynomial approximation. Refer to the bold portions of the quotations cited above.
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to apply a polynomial approximation taught in Lukas-Simonyi, using the pre-processing unit taught by Noone, to the profiles of Noone (see fig. [6B] of Noone) in order to better characterize the profiles.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Noone et. al. in view of Fujiwara et. al (JP 2021042988).
Regarding Claim 9:
Noone et. al (i.e., para. [0004, 0225, 0275]) teaches: for each object of the plurality of objects, respectively detecting anomalies on a surface of the object in question by: creating a depth profile, (see fig. [6B]) of the surface of the object, (para. [0007, 0009, 0017], fig. [6B])
Noone et. al teaches: pre-processing the depth profile by approximating a shape along a spatial dimension (see para. [0224]): “For example, a reference data set may comprise sensor data recorded by one or more sensors for an ideal, defect-free example of the object to be fabricated.” The reference data set for an ideal object being a “shape” – see fig. 6B) and subsequently subtracting the approximated shape from the depth profile in order to obtain a simplified profile (para. [0017, 0210, 0222-0224], fig. [6B]).
Noone et. al teaches: detecting the anomalies on the surface of the object by applying a machine learning algorithm to the simplified profile, the machine learning algorithm trained in order to detect anomalies in depth profiles, see para. [0225-0229, 0239].
Noone et. al fails to teach: “A method for discarding objects of a plurality of objects,” and “for each object of the plurality of objects, respectively determining whether the object in question is to be discarded based on the anomalies detected on the surface of the object; and for each object of the plurality of objects, respectively discarding the object in question when it has been determined that the object in question is to be discarded.”
Fujiwara et. al, paragraphs [0022-0024] teaches: “a method for discarding objects of a plurality of objects,” and “for each object of the plurality of objects, respectively determining whether the object in question is to be discarded based on the anomalies detected on the surface of the object; and for each object of the plurality of objects, respectively discarding the object in question when it has been determined that the object in question is to be discarded,” See the translation of paragraph [0022] below:
“The object to be inspected is not particularly limited. In this embodiment, an industrial product such as a tire is used as the object to be inspected. In this case, the appearance inspection method of the first invention is performed on the production line of the industrial product. Then, a product determined to have an appearance defect is discarded without being shipped, or is shipped after the defect is repaired.” (para. [0022])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Fujiwara’s method to discard objects containing defects identified by Noone’s method in order to improve quality control in manufacturing processes.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20210027442 A1, Price, R., System and method for Automated Surface Assessment, 2021.
Conclusion
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/J.A.H./Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857